This talk describes an application of the LinGO English Resource Grammar (ERG) (Flickinger 2000) to the problem of grammar checking for non-native speakers. The ERG is a broad-coverage precision HPSG for English, developed over the past 9 years and comprising over 70,000 lines of code. To create a prototype grammar checker, we have augmented the ERG with mal-rules, which produce well-formed semantic representations from ill-formed syntactic input. We then treat the problem of correcting the sentence as a kind of machine translation, and generate a well-formed sentence from the semantic representation using the core grammar (without the mal-rules).
One consequence of the broad-coverage of the grammar is ambiguity, such that most inputs have more than one possible parse. With the addition of mal-rules, ambiguity only increases. We are exploring handling the ambiguity on the parsing side with a maximum entropy model trained on a treebank (Oepen et al 2002), and on the generation side by aligning the generated output as closely as possible to the winning parse of the input.
Back to symposium main page